问R语言进行生信分析，GO分析goplot()反复报错？

rm(list = ls())  
load(file = 'step4output.Rdata')
library(clusterProfiler)
library(ggthemes)
library(org.Hs.eg.db)
library(dplyr)
library(ggplot2)
library(stringr)
library(enrichplot)


# 1.GO 富集分析----(用差异基因做富集分析)

#(1)输入数据：上调和下调基因的ENTREZID
gene_up = deg$ENTREZID[deg$change == 'up'] 
gene_down = deg$ENTREZID[deg$change == 'down'] 
gene_diff = c(gene_up,gene_down)

#(2)富集——运用函数enrichGO()
#以下步骤耗时很长，设置了存在即跳过
f = paste0(gse_number,"_GO.Rdata")
if(!file.exists(f)){
  ego <- enrichGO(gene = gene_diff,
                  OrgDb= org.Hs.eg.db,#分析基因的类别:人类?鼠?
                  ont = "ALL",
                  readable = TRUE)#"readable"让基因名称变得可读
  ego_BP <- enrichGO(gene = gene_diff,
                  OrgDb= org.Hs.eg.db, 
                  ont = "BP",
                  readable = TRUE)
  #ont参数：One of "BP"(生物学过程), "MF"(分子功能), and "CC"(细胞组件) subontologies, or "ALL" for all three.
  save(ego,ego_BP,file = f)
}
load(f)

#(3)可视化
#条形图
barplot(ego)
    #分面条形图
barplot(ego, split = "ONTOLOGY", font.size = 10, 
        showCategory = 5) + 
  facet_grid(ONTOLOGY ~ ., space = "free_y",scales = "free_y") 

#气泡图
dotplot(ego)
    #分面气泡图
dotplot(ego, split = "ONTOLOGY", font.size = 10, 
        showCategory = 5) + 
  facet_grid(ONTOLOGY ~ ., space = "free_y",scales = "free_y") 

#(3)展示top通路的共同基因(即P值比较小的基因)，要放大看。

#gl 用于设置下图的颜色
gl = deg$logFC
names(gl)=deg$ENTREZID
#Gene-Concept Network,要放大看
cnetplot(ego,
         #layout = "star",
         color.params = list(foldChange = gl),
         showCategory = 3)
goplot(ego_BP)